Selection of new sweetpotato hybrids for West Africa using accelerated breeding scheme and genotype × environment interaction under drought stress

West Africa is a dry region and drought tolerant sweetpotato cultivar was not reported. The objective of this study was to develop higher yielding drought tolerant sweetpotato hybrids following accelerated breeding scheme (ABS), and study G × E interaction. During advanced yield trial, the assessment of clones was conducted in six locations: four in Niger and two in Nigeria. Data were collected on storage root yield (SRY), harvest index (HI) and root dry matter content (DMC). Twenty-three hybrids were evaluated under drought and irrigation. Terminal drought was imposed. SAS and GenStat softwares were used for analyses. Based on drought susceptibility index (DSI), drought tolerant expression (DTE) and HI, clones 4 × 5 – 3, 9 × 7 – 1, 5 × 9 – 2, 3 × 6 – 2, and 3 × 12 – 3 were the best in SRY under drought stress and well-watered in combined data. Using AMMI stability value (ASV) and stability cultivar superiority (SCS), results revealed that the most superior cultivars were unstable. Clones 12 × 5 – 1 and 9 × 10 – 1 were recommended under drought for SRY stability combined with high DMC and high total carotene (TC). Under irrigation, the 13 × 8 – 2 is good candidate for stability across all locations combined with high DMC and medium TC, while clone 4 × 3 – 2, 13 × 8 – 2, 4 × 6 – 2 and 6 × 8 – 5 were stable SRY with high DMC. Therefore, these hybrids could be evaluated at on-farm trials to release the best to farmers.

Experimental design and drought management. Split plot arranged in Alpha Lattice Design with 2 replications was used as the experimental design in the six locations. The water regime (water stress and wellwatered) was main plot factor and genotype was the sub-factor. The spacing between sub-blocks, blocks and replications were 1, 1.5 and 2 m, respectively. All the trials were established from August to December 2021. Overhead irrigation was carried out for all the blocks for 8 weeks 4,26 , then terminal drought stress was imposed in two blocks while, the other two blocks were irrigated. For the well-watered (WW), irrigation was stopped one week before harvesting. Selection was based on laid down criteria 9 i.e., clones with storage root yield (SRY) greater than the trial grand mean were selected. Fertilizer  was applied at a rate of 6 g/stand at 4 and 8 six week after planting (WAP). First and second weeding were done at 4 and 8 WAP too. Storage roots were dug manually at 17 WAP. Data collection. The following data were collected: storage root yield estimated at tonnes/hectare (SRY t/ ha), harvest index (%HI) and dry matter content (%DMC). Sweetpotato descriptor 27 was used to record skin and flesh characteristics of storage root. The methods described by 28 were followed to determine dry matter content (DMC). A sample of 50 ± 5 g was collected from the medium free of disease storage roots of each genotype and kept in a paper sampling bag. Each storage root from each sample was peeled and cut from the middle to collect the sample. These samples were dried in an oven (Gravity Convection Incubator 4EC in Niger and Electrothermal oven, model DHG in Nigeria) at 70 °C for 72 h. Dried samples were weighed with an electronic balance and then, the DMC was calculated as follows: %DMC = [(Dry weight/Fresh weight) × 100]. Data analysis. Data on storage root yield (t/ha), harvest index (%HI) and root dry matter content (%DMC) were subjected for analysis of variance (ANOVA) across six locations under drought stress and well-watered using statistical analysis SAS program 9.3 29 . Data for drought and irrigation was analysed separately using SAS software. Then, in each water regime, data were analysed in three steps. In the first step, phenotypic data were analysed separately for each treatment in each location. In the second step, data was analysed for each treatment across locations of each country. Third step, data was analysed in each water treatment conditions across the six locations for G x E interaction. The mean differences among treatments were detected by LSD at 95% of confidence. The ANOVA showed significant G x E interaction effect, and this led to the partitioning of the genotype-by-environment interaction ( www.nature.com/scientificreports/ tion (AMMI), and GGE biplot models in GenStat software 17th edition 30 . This allowed to check the adaptability and to determine the stability of the 23 hybrids evaluated across the six locations of Niger and Nigeria. The linear model of GEI analysis using AMMI analysis by 31 is:  www.nature.com/scientificreports/ where: Ȳ ijk = the yield of the rth replicate of ith genotype in jth environment, µ is the overall mean, G i = is the main effect of the ith genotype, Ej = is the main effect of jth environment, λ k = the square root of the eigen value of the kth IPCA axis, α ik and ϒ jk = the principal component scores for IPCA axis k of the ith genotypes and the jth environment, ρ ij = the deviation from the model. To estimate stability of the genotypes, AMMI stability value (ASV) was computed according to 32 as follow: www.nature.com/scientificreports/ where: IPCA1 and IPCA2 were the Interaction Principal Component Analysis axes 1 and 2. They are the first and second from IPCA scores for each genotype from the AMMI analysis. SSIPCA1 and SSIPCA2 were the sum of square of IPCA 1 and 2. Superiority measure of stability (P i ) was computed according to the formula given by 33 to determine superiorities among the cultivars in the studied environments. The formula was as follow: where: X ij = the yield of the ith genotype in the jth site or environment, M j = the maximum response among all progenies in the jth site or environment, n = number of site or environments.
The linear model of GGE biplot based on Singular Value Decomposition (SVD) of the principal component (PC) was given by 11 : where: Ȳ ij is the yield performance in location j from genotype i, µ i is the overall average, β j is the main effect of location j, k is the number of principal components (PC), λ k is the singular value of the kth PC, α ik and ϒ jk are the scores of ith genotype and jth environment respectively for PC k , ε ij is the error of genotype i in location j.
Also, based on stress tolerance index, clones with high yield potential and high stress tolerance were identified. For each clone, based on their storage root yield under water deficit and normal irrigation, the drought tolerance indices below were computed 34-36 : where Stress Intensity Index, SII = 1 − ϒs ϒp .
where: Ys = Yield of a genotype under drought deficit, Yp = Yield of a genotype under normal irrigation, ϒs = Mean yield of all genotypes under drought deficit, ϒp = Mean yield of all genotypes under normal irrigation.
Regarding the physico-chemical analysis, sample was taken from each clone and analysed in duplicate for total carotene content at the National Root Crop Research Institute (NRCRI) in Nigeria (Table 1). Soil chemical and physical analysis of six locations was analysed at Ebonyi state university (EBSU) ( Table 2).

Results
Combined ANOVA of three traits. A combined analysis of variance under drought stress and normal condition was performed for SRY, HI and DMC ( Table 3). The results exhibited very highly significant (p < 0.001) difference among the six environments and the tested clones for SRY, HI and DMC under managed drought and well-watered conditions, except SRY under irrigation (p < 0.05). Besides, the results revealed very highly significant (p < 0.001) genotype x environment (G × E) interaction effects for all the traits under both conditions except SRY under irrigation where significant differences were observed (p < 0.05).
(3) Table 3. Combined analysis of variance of storage root yield, harvest index and dry matter in multienvironment testing under drought stress and irrigation. *Significant at 5%, **Significant at 1%, ***Significant at 0.1%, ns not significant, DF degree of freedom, SRY storage root yield, HI harvest index, DMC dry matter content, DS drought stress, WW well-watered, CV coefficient of variation, R 2 R-square.  Table 4 showed that 14 out of 23 clones had drought susceptible index (DSI) less than 1.0, while seven had SRY greater than grand mean under drought (7.28 t/ha) and 10 clones had values higher than the grand mean under irrigation (14.88 t/ha). Genotypes 1 × 9 -5, 6 × 8 -5, 8 × 6 -1 and 13 × 8 -2 recorded DSI less than 1.0, each with respective values of: 0.92, 0.62, 0.82 and 0.84. The performance of the four clones for SRY under non irrigated condition were greater than the grand mean (7.28 t/ha) moreover, the same clones had higher means than the grand mean (14.88 t/ha) under irrigation. These genotypes also had high values for drought tolerance expression (DTE) and percent of harvest index (%HI) greater than grand mean under drought (29.26%) and under irrigation (35.31%). Twelve hybrids had HI greater than grand mean under drought (29.26%) and 11 under irrigation (35.31%). However, genotype 7 × 6 -1 had the highest SRY under drought (13.12 t/ha) and irrigation (30.47 t/ha), but DSI was greater than 1.0. The geometric mean productivity (GMP) for the clones 4 × 5 -3 (5.83), 9 × 7 -1 (6.57), 5 × 9 -2 (6.95), 3 × 6 -2 (7.00), and 3 × 12 -3 (7.06) were low, but their respective SRY under both conditions were lesser than grand mean. For root dry matter content (DMC), the value without irrigation was higher than with irrigation in most of the cases with the average without irrigation (31.58%) and with irrigation (
GGE biplot for storage root yield, harvest index and root dry matter content. Storage root yield (SRY). GGE biplot for storage root yield (SRY) under drought and irrigation are presented in Fig. 2.
The "which-won-where" for SRY (Fig. 2) showed that the first two principal components axes (PCs) explained 84.31% of total variation under drought (Fig. 2a) and 83.54% under irrigation (Fig. 2b). Genotypes to the left of the line that passe through the biplot origin line are with below average value, whereas genotypes to the right are with above average values of the interest traits The GGE biplot under drought showed that clones C15 (7 × 6 -1) and C12 (6 × 8 -5) had high yield and adaptability for E5, E1, E3 and E4, since they were located at the vertices of the polygons, while C1 (1 × 2 -1), C3 (1 × 9 -5) and C16 (8 × 6 -1) had high yield but with low adaptability at E5, E1, and E3. Under irrigation clones C14 (6 × 13 -1) and C15 (7 × 6 -1) had high SRY and widely stable at E3, E4 and E2. Under drought C15 performed well at E1 and E5, while, C12 performed better at E3 and E4. Under irrigation C20 was good at three environments (E1, E3 and E5) as well as C15 (E4, E3 and E5), while C14 performed better at E2 and E6. Three mega-environments were identified by GGE biplot under drought stress, in which E2 constitute the first mega-environment, E1, E5 and E6 the second and E3 and E4 the third mega-environment. Under irrigation, two Table 5. Storage root yield, stability cultivar superiority and stability parameters of sweetpotato hybrids under drought and well-watered conditions. C clone, AYT advanced yield trial, SRY storage root yield, IPCA1 interaction principal component analysis 1, IPCA2 interaction principal component analysis 2, SCS stability cultivar superiority, ASV AMMI stability value, DS drought stress, WW well-watered.  Figure 3 presents the GGE biplots showing "which won where" or which clones are best for which location for harvest index (%HI). Under drought condition PCs 1 and 2 explained 48.52% and 20.88% % of the total variation, while under irrigation the two PCs PC1 64.07% and PC2 10.99%, respectively. Clones C14, C10, C9, and C1 were found by GGE biplot as the highest HI (greater than the overall average of 29.26%) and most stable genotypes at E1, E2, E3, and E4 respectively under drought condition, while clones C5, C17, C20, and C2 had HI less than the average. Under well-watered condition clones C9, C14, and C1 were identified by GGE Biplot as the promising and stable clones across all the four locations of Niger with HI above the overall average (31.35%), while C8, C6, C5, C21, and C10 registered the HI below the average.

Root dry matter content (%DMC).
Results of GGE Biplot of 23 hybrid clones across the six locations in Niger and Nigeria for root dry matter content (DMC) is presented in Fig. 4. The study revealed that the two PCs under drought accounted 72.06% of the total variation (Fig. 4a), while under optimum condition of good watering, 82.32% was explained (Fig. 4b)

Discussions
Combined ANOVA. The combined analysis of variance of SRY, HI and DMC across environments, genotypes, and genotypes by environments interaction indicated that the SRY, HI and DMC under both drought stress and well-watered condition were highly affected by environments. The highly significant (p < 0.001) difference among the tested genotypes for the three characters under drought stress (DS) and well-watered (WW) indicates the genetic causes of variation. The highly significant (p < 0.001) difference which existed between the six environments suggests significant differences among the environments. Genotypes' performance was influenced by genetic constitution and the environment where they were grown 10,12 . The inconsistencies in the performances of the genotypes resulted in cross-over interaction (a difference in ranking of a genotype from one environment to another). This suggests that some genotypes were sensitive and not stable within treatments and www.nature.com/scientificreports/ from one environment to another. Similar results were reported on SRY and DMC by 16,18,19,37 . In Niger, Nigeria and other West African countries, rainfall is irregular and this makes breeding for drought a complex endeavour. G × E causes difficulty in selection of genotypes with wide adaptation, which may delay the cultivar release 22 . Drought can be random, periodic, or permanent, occurring late, early, or in the middle of the crop season. In this study terminal drought was imposed at advanced yield trial in ABS, which started at the onset of root initia-  www.nature.com/scientificreports/ tion. In sweetpotato breeding programs, knowledge on structure of G x E is therefore crucial to facilitate recommendations of genotypes for cultivar release, and to make informed choices regarding selection of cultivars with specific or wide adaptation 16 . Drought parameters. In this study, DSI, DTE and HI, exhibited that clones 4 × 5 -3, 9 × 7 -1, 5 × 9 -2, 3 × 6 -2, and 3 × 12 -3 were the best in high SRY under the two moisture-management conditions across all the six locations in Niger and Nigeria. The results for these five genotypes, showed that their DSI were less than 1.0 with high DTE, and high HI. Genotypes with DSI below 1.0 were drought tolerant cultivars, and have stable yield in both moisture-managed conditions, while the genotypes with DSI above 1.0 are considered to be highly susceptible to drought with poor yield stability 4,34 . Andrade et al. 18 recorded the highest DTE (96.22%) and highest %HI under drought (53.52%), and under irrigation (47.07%). Sweetpotato breeding programs have significantly improved SRY production due to improved harvest index 18 . Genotypes with high GMP are desired, since it indicates that the difference between the two treatments are small. However, in this study, clones that gave high GMP had high SRY in both moisture-managed conditions. This result corroborated with the finding of 18 39 reported larger IPCA1 in one environment out of three in their study. The higher the positive or negative IPCA value, the higher, the adaptability of a particular genotype to a specific environment 20,40 . Low values of IPCA1 and IPCA2 under drought at Kollo (E1) indicates that the environment had more influence on the clones than the genetic effects. This is in consonance with other reports 21,39 . Genotypes with low value of stability cultivar superiority (Pi) are considered the best stable clones 33 . Clones 7 × 6 -1, 8 × 6 -1, 1 × 9 -5 and 1 × 2 -1 were the highest yielding hybrids under drought and well-watered conditions in E1, E3, E4, and E5. These genotypes were found to be less affected by the environmental factors, therefore, they could be proposed for high storage root yield production across these locations. The highest genotype in particular environment is the more unstable. Unstable genotypes were influenced by environment; thus, they may be proposed for storage yield production under specific condition for a particular location. In this study, AMMI stability value (ASV) was used to check the stability of the hybrids across Niger and Nigeria agro-ecologies. The smaller the ASV the more stable the genotype across locations, but a larger value for ASV in a location indicates better adaptation of the genotype to a specific environment 41,42 . Maulana et al. 43 used ASV and genotype stability index (GSI) to rank the stability of 23 genotypes evaluated in three locations. The ASV has also been used in Indonesia to select more stable sweetpotato 20,21 . In this study, when coupling ASV and Pi; in this study, genotypes that were ranked as highest yielder Pi were not stable according to ASV. Therefore, there is a challenge for ranking high and stable genotypes across the six studied locations of Niger and Nigeria. High and stable yielding genotypes are ideal to both farmers and plant breeders 44,45 . GGE biplot for SRY, HI and DMC. Storage root yield. Widely and narrow adapted clones were reported in this study without and with irrigation across six tested locations in Niger and Nigeria. Each category has its advantages. In fact, stable clone may adapt to many locations, while the narrowly adaptable high yielders could be better suited to particular/ specific locations. According to 46 cultivars with adaptation to a particular environment (i.e., responsive and unstable) have that advantage to respond to environmental changes compared to widely adapted (i.e., non-responsive and stable). Locations in different mega-environment showed that clones located in the locations belonging to the same mega-environment have similar yields. Mega-environments displayed various high yielding genotypes, and this indicates the presence of cross-over G × E interaction and inconsistent performance of the tested genotypes over the environments 31  www.nature.com/scientificreports/ best genotypes in Niger and C10, C21, C17, and C3 in Nigeria. These genotypes could be selected for suitability to particular locations for high dry matter content they are also good candidates for industrial purposes. Genotypes with raw DMC > 35% are suitable products in processing industry 48 . Therefore, breeding for high DMC, could be a focus of sweetpotato plant breeder. In comparison, with other studied traits (SRY and %HI), DMC was widely adapted to most of the locations. This confirmed findings of 16 who stated that DMC is less influenced by the environment.
To complete the ABS method, these selected genotypes could be evaluated at on-farm trials for stability and for sensitiveness according to the response of their respective environments. Breeder should consider the challenges for selecting not only cultivar with high stability and high SRY, but also candidate with the attributes of high SRY, high DMC and high TC.

Data availability
The datasets of the current study can be requested from corresponding author with strong reason.